ENHANCED PARALLEL DEEP LEARNING FOR MALWARE DETECTION (EPDL-MD) MODEL

Abstract

The number of cyberattacks caused by malware targeting critical sectors, such as energy systems, telecommunications, healthcare, and finance, is rapidly increasing worldwide. The evolution of malware has made detection techniques more challenging, resulting in financial losses and reduced productivity. To address this issue, this paper presents the Enhancement of Parallel Deep Learning for Malware Detection (EPDL-MD) model. This model focuses on improving the parallel convolutional neural network (CNN) architecture. The performance of the CNN is influenced by its hyperparameters, and the enhancements have led to an increase in accuracy and learning rate. The experiment utilized 176,000 malware samples, which were sourced from 86 distinct malware families and one benign family. Based on the analysis and experiments, the EPDL-MD model has achieved an impressive accuracy rate of 99%.